Amazon 公布 34 位 Build on Trainium 研究奖获得者

Amazon announces recipients of the Build on Trainium program, a $110 million credit initiative supporting AI research at 30 universities.

Build on Trainium is a $110 million credit program focused on AI research and university education aimed to support the next generation of innovation and development on AWS Trainium. The program provides compute credits to novel AI research on Trainium, investing in leading academic teams to build innovations in critical areas including new model architectures, ML libraries, optimizations, large-scale distributed systems, and more.

This announcement includes awards funded under the Fall 2025 Build on Trainium: Responsible AI call for proposals. Proposals were reviewed for the quality of their scientific content and their potential to impact both the research community and society. This cycle’s focus on Responsible AI invited proposals addressing five priority topics: AI safety and alignment, multi-lingual language models, representation engineering, sustainability and small language models, and deep learning models for synthetic data generation—all leveraging AWS Trainium infrastructure. The recipients have access to more than 700 Amazon public datasets and can utilize AWS AI/ML services and tools through their AWS Promotional Credits, are assigned an Amazon research contact who offers consultation and advice, and benefit from AWS Trainium resources, such as tutorials and hands-on sessions.

Build on Trainium represents AWS's commitment to democratizing AI research through collaborative partnership with academia.

"Build on Trainium gives the next wave of AI researchers powerful, scalable access to Amazon's purpose-built AI chips, so the only limit is their imagination, not their compute budget," said Yida Wang, AWS AI Principal Applied Scientist. "By leveraging the support from Build on Trainium, University of Illinois Urbana-Champaign researchers are studying topology-aware parallelization strategies for large-scale mixture-of-experts models with as many as one trillion parameters on up to 1,024 Trainium chips. At the University of Washington, researchers are developing an inference-optimization framework that raises token efficiency for everyone building on Trainium, with the goal to deliver portable, high-performance LLM inference on Trainium."

Viveck Cadambe

Georgia Institute of Technology

Yujun Cai

The University of Queensland

Haipeng Chen

College of William and Mary

DELA: Editable Diffusion Language Models

Tianlong Chen

Saadia Gabriel

University of California Los Angeles

Haewon Jeong

University of California Santa Barbara

Haojian Jin

University of California San Diego

Marios Kogias

Imperial College London

Towards Deterministic Model Inference

Institute for Decentralized AI (ADAI)

Xiaoxiao Li

The University of British Columbia

Yingcong Li

New Jersey Institute of Technology

Zhijian Liu

University of California San Diego

Songtao Lu

Yao Lu

UCL - University College London

Sasa Misailovic

University of Illinois at Urbana-Champaign

Massachusetts Institute of Technology

LLM Hallucination Detection and Mitigation

Jaideep Srivastava

University of Minnesota Twin Cities

Cheng Tan

Northeastern University

Towards Reliable and Trustworthy LLM Services with ϵ-correctness

Yue Wang

University of Central Florida

Yang Wang

University of Illinois at Urbana-Champaign

Bigger Models, Bigger Risks? Investigating the Safety Landscape of LLM Scaling

Xiaokui Xiao

National University of Singapore